Dataset Information

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution.

ABSTRACT: Internal coordination models hold that early nervous systems evolved in the first place to coordinate internal activity at a multicellular level, most notably the use of multicellular contractility as an effector for motility. A recent example of such a model, the skin brain thesis, suggests that excitable epithelia using chemical signaling are a potential candidate as a nervous system precursor. We developed a computational model and a measure for whole body coordination to investigate the coordinative properties of such excitable epithelia. Using this measure we show that excitable epithelia can spontaneously exhibit body-scale patterns of activation. Relevant factors determining the extent of patterning are the noise level for exocytosis, relative body dimensions, and body size. In smaller bodies whole-body coordination emerges from cellular excitability and bidirectional excitatory transmission alone. Our results show that basic internal coordination as proposed by the skin brain thesis could have arisen in this potential nervous system precursor, supporting that this configuration may have played a role as a proto-neural system and requires further investigation.

SUBMITTER: de Wiljes OO

PROVIDER: S-EPMC4569742 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution.

de Wiljes Oltman O OO van Elburg Ronald A J RA Biehl Michael M Keijzer Fred A FA

Frontiers in computational neuroscience 20150915

Internal coordination models hold that early nervous systems evolved in the first place to coordinate internal activity at a multicellular level, most notably the use of multicellular contractility as an effector for motility. A recent example of such a model, the skin brain thesis, suggests that excitable epithelia using chemical signaling are a potential candidate as a nervous system precursor. We developed a computational model and a measure for whole body coordination to investigate the coor ...[more]

PMID: 26441620

Similar Datasets

Project description:In many species, vocal communication is essential for coordinating social behaviors including courtship, mating, parenting, rivalry, and alarm signaling. Effective communication requires accurate production, detection, and classification of signals, as well as selection of socially appropriate responses. Understanding how signals are generated and how acoustic signals are perceived is key to understanding the neurobiology of social behaviors. Here we review our long-standing research program focused on Xenopus, a frog genus which has provided valuable insights into the mechanisms and evolution of vertebrate social behaviors. In Xenopus laevis, vocal signals differ between the sexes, through development, and across the genus, reflecting evolutionary divergence in sensory and motor circuits that can be interrogated mechanistically. Using two ex vivo preparations, the isolated brain and vocal organ, we have identified essential components of the vocal production system: the sexually differentiated larynx at the periphery, and the hindbrain vocal central pattern generator (CPG) centrally, that produce sex- and species-characteristic sound pulse frequencies and temporal patterns, respectively. Within the hindbrain, we have described how intrinsic membrane properties of neurons in the vocal CPG generate species-specific vocal patterns, how vocal nuclei are connected to generate vocal patterns, as well as the roles of neurotransmitters and neuromodulators in activating the circuit. For sensorimotor integration, we identified a key forebrain node that links auditory and vocal production circuits to match socially appropriate vocal responses to acoustic features of male and female calls. The availability of a well supported phylogeny as well as reference genomes from several species now support analysis of the genetic architecture and the evolutionary divergence of neural circuits for vocal communication. Xenopus thus provides a vertebrate model in which to study vocal communication at many levels, from physiology, to behavior, and from development to evolution. As one of the most comprehensively studied phylogenetic groups within vertebrate vocal communication systems, Xenopus provides insights that can inform social communication across phyla.

Project description:BackgroundA well-trained and equitably distributed workforce is critical to a functioning health system. As workforce interventions are costly and time-intensive, investing appropriately in strengthening the health workforce requires an evidence-based approach to target efforts to increase the number of health workers, deploy health workers where they are most needed, and optimize the use of existing health workers. This paper describes the Malawi Ministry of Health (MoH) and collaborators' data-driven approach to designing strategies in the Human Resources for Health Strategic Plan (HRH SP) 2018-2022.MethodsThree modelling exercises were completed using available data in Malawi. Staff data from districts, central hospitals, and headquarters, and enrollment data from all health training institutions were collected between October 2017 and February 2018. A vacancy analysis was conducted to compare current staffing levels against established posts (the targeted number of positions to be filled, by cadre and work location). A training pipeline model was developed to project the future available workforce, and a demand-based Workforce Optimization Model was used to estimate optimal staffing to meet current levels of service utilization.ResultsAs of 2017, 55% of established posts were filled, with an average of 1.49 health professional staff per 1000 population, and with substantial variation in the number of staff per population by district. With current levels of health worker training, Malawi is projected to meet its establishment targets in 2030 but will not meet the WHO standard of 4.45 health workers per 1000 population by 2040. A combined intervention reducing attrition, increasing absorption, and doubling training enrollments would allow the establishment to be met by 2023 and the WHO target to be met by 2036. The Workforce Optimization Model shows a gap of 7374 health workers to optimally deliver services at current utilization rates, with the largest gaps among nursing and midwifery officers and pharmacists.ConclusionsGiven the time and significant financial investment required to train and deploy health workers, evidence needs to be carefully considered in designing a national HRH SP. The results of these analyses directly informed Malawi's HRH SP 2018-2022 and have subsequently been used in numerous planning processes and investment cases in Malawi. This paper provides a practical methodology for evidence-based HRH strategic planning and highlights the importance of strengthening HRH data systems for improved workforce decision-making.

Project description:Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Dataset Information

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution.

Publications

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets